발견 중심의 숙주-병원균 상호 작용을 위한 라벨 프리 정량적 프로테오믹스 워크플로우

Executive Industry Relevance

This label-free quantitative proteomics workflow enables simultaneous profiling of host and pathogen proteomes during infection, providing unbiased protein abundance data critical for target validation in infectious disease research. By identifying infection-associated fungal proteins and host response signatures, the method supports mechanistic de-risking of anti-virulence strategies and informs preclinical target selection. Its transferability across host-pathogen systems enhances portfolio-wide applicability for antifungal and immunomodulatory discovery programs.

Strategic Applications in Biopharma R&D

Early Discovery & Target Validation

• Scientific Value: Enables interrogation of therapeutic hypotheses by identifying pathogen proteins exclusively produced during infection, representing novel virulence-associated targets.
• Scientific Value: Clarifies host-pathogen interaction pathways through correlated abundance changes in both proteomes, supporting functional target validation.
• Scientific Value: Reduces mechanistic ambiguity by providing quantitative, reproducible infection-state proteomic profiles for de-risking target hypotheses.

Screening & Assay Development

• Operational Value: Prepares validated biological systems (infected macrophages) for downstream assay development with standardized infection conditions and MOI optimization.
• Operational Value: Generates quantitative, label-free proteomic outputs suitable for assay standardization and reproducibility assessment in screening campaigns.
• Operational Value: Supports scalable platform reuse across diverse pathogens due to the universal proteomics and bioinformatics pipeline design.

Translational & Preclinical Research

• Translational Value: Identifies disease-relevant host proteins responding to microbial invasion, enabling biomarker alignment for preclinical validation.
• Translational Value: Ensures continuity from discovery through preclinical work by linking infection-state proteomic shifts to pathogenic mechanisms.
• Translational Value: Informs risk-adjusted advancement decisions by highlighting proteins essential for virulence and host defense mechanisms.

Pipeline & Workflow Integration

The workflow integrates into early discovery biology through host-pathogen interaction profiling, supports screening readiness via standardized infection models, and enables translational research by connecting proteomic changes to virulence and immune response mechanisms.

• Discovery Biology: Supports hypothesis testing and pathway clarification by quantifying infection-induced changes in both pathogen and host proteomes.
• Screening: Delivers assay-ready biological systems with optimized MOI and time points, ensuring reproducible infection models for compound evaluation.
• Analytics: Provides label-free quantitative measurements and statistical outputs (e.g., Benjamini-Hochberg corrected t-tests) to compare conditions and identify significantly altered proteins.
• Translational Research: Links proteomic signatures to pathogenic mechanisms and host defense responses, supporting biomarker discovery and preclinical continuity.
• Enterprise Reuse: Establishes a reusable proteomics and bioinformatics pipeline applicable to multiple pathogens and immune systems, reducing redundant method development.

Operational & Enterprise Impact

• Scientific Value: Increases predictive confidence in target validation through unbiased, quantitative infection-state proteomic profiling.
• Operational Value: Enhances reproducibility and standardization via optimized coculture, washing, and lysis protocols with validated QC metrics (PCA, clustering, 95-96% replicate reproducibility).
• Strategic Value: Improves go/no-go decisions by revealing critical virulence factors and host response networks, reducing late-stage biological risk in antifungal programs.
• Portfolio Impact: Enables risk-adjusted prioritization of targets based on infection-specific protein expression and pathway involvement.

Implementation Considerations

• Requires expertise in mammalian and fungal cell culture, proteomics sample preparation, and mass spectrometry data analysis.
• Dependent on access to LC-MS/MS instrumentation, label-free quantification software, and bioinformatics tools for PCA, clustering, and differential expression analysis.
• Necessitates cross-team standardization of infection parameters (MOI, time points) and sample handling to ensure reproducibility across studies.
• Involves adaptation considerations for different pathogen-host systems, including optimization of fungal cell preparation and macrophage confluency.
• Practical limitations include the technical challenge of detecting low-abundance fungal proteins amid high background host signals, requiring sufficient infection depth and sensitivity.

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